VLDB 2026 Research / reviewers in the wild / expert
Elias Eid
dblp:250/1274
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Program verification · 44% Empirical software engineering · 44% Requirements engineering and software design · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program verification
formal modeling |
0.7 | 1 | 2023 | Static Profiling of Alloy Models · IEEE Trans. Software Eng. 2023 |
Empirical software engineering
mining software repositories |
0.7 | 1 | 2023 | Static Profiling of Alloy Models · IEEE Trans. Software Eng. 2023 |
Requirements engineering and software design
model-driven engineering |
0.2 | 1 | 2023 | Static Profiling of Alloy Models · IEEE Trans. Software Eng. 2023 |
Methods — techniques the papers use, named apart from their topics
static analysis · 0.7XPath querying · 0.7ANTLR pattern matching · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Static Profiling of Alloy ModelsabstractModeling of software-intensive systems using formal declarative modeling languages offers a means of managing software complexity through the use of abstraction and early identification of correctness issues by formal analysis. Alloy is one such language used for modeling systems early in the development process. Little work has been done to study the styles and techniques commonly used in Alloy models. We present the first static analysis study of Alloy models. We investigate research questions that examine a large corpus of 1,652 Alloy models. To evaluate these research questions, we create a methodology that leverages the power of ANTLR pattern matching and the query language XPath. Our research questions are split into two categories depending on their purpose. The Model Characteristics category aims to identifywhatlanguage constructs are used commonly. Modeling Practices questions are considerably more complex and identifyhowmodelers are using Alloy's constructs. We also evaluate our research questions on a subset of models from our corpus written by expert modelers. We compare the results of the expert corpus to the results obtained from the general corpus to gain insight into how expert modelers use the Alloy language. We draw conclusions from the findings of our research questions and present actionable items for educators, language and environment designers, and tool developers. Actionable items for educators are intended to highlight underutilized language constructs and features, and help student modelers avoid discouraged practices. Actionable items aimed at language designers present ways to improve the Alloy language by adding constructs or removing unused ones based on trends identified in our corpus of models. The actionable items aimed at environment designers address features to facilitate model creation. Actionable items for tool developers provide suggestions for back-end optimizations. Elias Eid, Nancy A. Day |
IEEE Trans. Software Eng. | 1 |